Memoising
Memoising is similar to caching, however it only applies to steps within a run, and serves a subtly different purpose. With caching, a cache hit still results in a new step entity, but the result will be shared. Memoising is more lightweight because the existing execution is referenced directly, rather than creating a new step which references the existing result.
Memoising can be used as a way of optimising runs (by sharing a result), and also to make debugging runs easier.
Enable memoising of a task with the memo option:
@task(memo=True)
def fetch_user(user_id):
...
Memoised steps are indicated in the web UI with a pin icon.
As with caching, explicitly clicking the 're-run' button for a step will force the step to be re-run, even if it's memoised. Then subsequent memoising will use the new step execution.
If a step is manually re-run in a child environment, the memoised results will be used (but memoised results from the child environment aren't availble to the parent). This follows the same rules as caching.
For debugging
Memoising provides several benefits for debugging:
-
Memoising a task with side effects (e.g., sending a notification e-mail) means you can re-run the whole run (or part of it) without that side-effect happening.
-
Memoising slow tasks allows you to fix bugs that are occuring elsewhere in the workflow.
This is particularly useful when re-running a workflow from a production environment in a development environment (assuming the production environment is configured as an ancestor of the development environment). By liberally memo-ising tasks, specific steps can be re-run in the development environment without re-running downstream steps.
For optimisation
Memoising can also be used as an optimisation for workflows. For example, if a resource needs to be used in multiple parts of a workflow, rather than passing around that resource, the task to fetch it can be memoised:
@task(memo=True)
def fetch_user(user_id):
...
@task()
def send_email(user_id):
user = fetch_user(user_id)
...
@task()
def send_notification(user_id):
user = fetch_user(user_id)
...
In this case, the fetch_user task will only be executed once for the run, even if steps are re-run (provided the user ID doesn't change).
Memo parameters
As with caching, by default the memoisation considers all arguments. This can be changed by specifying the individual parameters:
@task(memo=["machine_id"])
def apply_configuration(machine_id, config)
...
In this case, the function to apply configuration to a machine is only run once for the specified machine, regardless of whether the configuration itself changes. This can allow you to make changes to a workflow and re-run it with (some) confidence that a new configuration won't be applied.